Once validated models are created and they match what operators want, the final step is to deploy the models on live data. This is when we actually deliver value in real-time to help operators address problems and make changes quickly. All too often, model training and deployment are two very
Artificial intelligence (AI) and machine learning (ML) promise to drastically improve the efficiency and output of today’s oilfield. Here are just some of the objectives we are striving for: AI models can monitor each well 7 days a week for 24 hours a day, automatically detecting and diagnosing any sub-optimal
One of the most difficult challenges of digital oilfield projects is requiring people who would not choose to work together, to collaborate for success. Operators and Data Scientists tend to be very different people, and these types of projects push them to work together, typically under fire from management to
One of the most frequently quoted problems in the media is the shortage of data science and AI capabilities in terms of trained people and practical know-how. But this is changing with more emphasis on data science and analytics education. The next challenge is finding solvable problems that can actually
Click here to request access to this replay! As Upstream producers assess the colossal changes of 2020 and strategize for the new year, OspreyData is proposing ways to harness the Digital Field to confront challenges head-on and thrive in a reshaped landscape. Don’t miss the ideas presented by Professor of
Gas Lift Optimization Models is the topic of our discussion today. Recently, we did a series of ROI case studies to demonstrate the impact of early detection on your bottom line. In today’s blog, we are sharing a sample of 60 days of results of our gas injection recommended implementation.